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--- |
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license: apache-2.0 |
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tags: |
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- Finance-sentiment-analysis |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: bert-base-finance-sentiment-noisy-search |
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results: [] |
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widget: |
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- text: "Third quarter reported revenues were $10.9 billion, up 5 percent compared to prior year and up 8 percent on a currency-neutral basis" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-finance-sentiment-noisy-search |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on Kaggle finance news sentiment analysis with data enhancement using noisy search. The process is explained below: |
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1. First "bert-base-uncased" was fine-tuned on Kaggle's finance news sentiment analysis https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news dataset achieving accuracy of about 88% |
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2. We then used a logistic-regression classifier on the same data. Here we looked at coefficients that contributed the most to the "Positive" and "Negative" classes by inspecting only bi-grams. |
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3. Using the top 25 bi-grams per class (i.e. "Positive" / "Negative") we invoked Bing news search with those bi-grams and retrieved up to 50 news items per bi-gram phrase. |
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4. We called it "noisy-search" because it is assumed the positive bi-grams (e.g. "profit rose" , "growth net") give rise to positive examples whereas negative bi-grams (e.g. "loss increase", "share loss") result in negative examples but note that we didn't test for the validity of this assumption (hence: noisy-search) |
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5. For each article we kept the title + excerpt and labeled it according to pre-assumptions on class associations. |
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6. We then trained the same model on the noisy data and apply it to an held-out test set from the original data set split. |
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7. Training with couple of thousands noisy "positives" and "negatives" examples yielded a test set accuracy of about 95%. |
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8. It shows that by automatically collecting noisy examples using search we can boost accuracy performance from about 88% to more than 95%. |
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Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown in the attached pdf: |
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https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing |
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## Model description |
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BERT model trained on noisy data from search results. See PDF for more details. |
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## Intended uses & limitations |
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Intended for use on finance news sentiment analysis with 3 options: "Positive", "Neutral" and "Negative" |
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To get the best results feed the classifier with the title and either the 1st paragraph or a short news summarization e.g. of up to 64 tokens. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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